Script to work with Satellite Embeddings dataset bands derived from GEE

Read and manipulate data

Load libraries

library(signal)
library(tidyverse)
library(here)
library(lubridate)
library(dtplyr)
library(sf)
library(knitr)
library(mgcv)
library(future)
library(furrr)
library(progressr)
library(pracma)

Set a simple console progress bar

handlers("txtprogressbar") # Simple console progress bar

Supress package messages

suppressPackageStartupMessages({
  library(mgcv)
  library(nlme)
})

Load Resurvey db

db_Europa_allobs <- read_csv(
  here("data", "clean", "db_Europa_allobs.csv")) %>%
  select(PlotObservationID, EUNISa_1, EUNISa_1_descr,
         EUNISa_2, EUNISa_2_descr) %>%
  mutate(PlotObservationID = factor(PlotObservationID),
         EUNISa_1 = factor(EUNISa_1), EUNISa_2 = factor(EUNISa_2))

Define printall function

printall <- function(tibble) {
  print(tibble, width = Inf)
  }

Read files with band data

I got these files using GEE.

These files contain all observations in the ReSurvey database from 2016 onward. In order to avoid computation problems in GEE, biogeographical units that contain more than 4500 points have been subdivided in ArcGIS.

# Set the folder path
folder_path <- "C:/Data/MOTIVATE/MOTIVATE_RS_data/Satellite_Embeddings"

# List CSV files
csv_files <- list.files(folder_path, full.names = TRUE, recursive = TRUE)

# Function to extract biogeo and unit from the filename
extract_info <- function(filename) {
  first_word <- strsplit(filename, "_")[[1]][1]
  biogeo <- str_extract(first_word,
                        "^(ALP|ANA|ARC|ATL|BLACKSEA|BOR|CON|MACARONESIA|MED|PANONIA|STEPPIC)")
  unit <- str_remove(first_word, biogeo)
  if (is.na(unit) || unit == "") unit <- NA_character_
  list(biogeo = biogeo, unit = unit)
  }


# Define column types: force RSrvypl to character, others auto-detected
custom_col_types <- cols(
  RSrvypl = col_character(),
  RSrvyst = col_character(),
  default = col_guess()
)

# Read and process each file
data_list <- lapply(csv_files, function(file) {
  info <- extract_info(basename(file)) # Use only the filename
  
  # Read the file
  df <- read_csv(file, col_types = custom_col_types) %>%
    # Remove columns that give column type problems when combining data
    select(-starts_with("EUNIS"), -starts_with("ReSurvey")) %>%
    mutate(biogeo = info$biogeo, unit = info$unit)
  
  return(df)
  })

# Combine all data
data_RS_SatEmb_bands <- bind_rows(data_list) %>%
  rename(PlotObservationID = PltObID)

# View the resulting tibble
print(data_RS_SatEmb_bands)

# Counts per biogeo and unit
print(data_RS_SatEmb_bands %>% count(biogeo, unit), n = 100)

Remove unneded cols

data_RS_SatEmb_bands <- data_RS_SatEmb_bands %>%
  select(-X1, -X2, -X3)

Add EUNIS codes

final_RS_data <- data_RS_SatEmb_bands %>% 
  mutate(PlotObservationID = as.factor(PlotObservationID)) %>%
  left_join(db_Europa_allobs)

Add canopy height data

Read the data:

data_RS_CH <- read_csv(
  "C:/Data/MOTIVATE/MOTIVATE_RS_data/Canopy_Height_1m/Europe_points_CanopyHeight_1m.csv")
db_Europa <- read_csv(
  here("..", "DB_first_check", "data", "clean","db_Europa_20250107.csv")
  )

data_RS_CH_ID <- db_Europa %>%
  select(PlotObservationID, obs_unique_id) %>%
  right_join(data_RS_CH %>%
              # Rename to be able to join on this column
              rename(obs_unique_id = obs_unique)) %>%
  select(PlotObservationID, canopy_height)

Join:

final_RS_data <- final_RS_data %>%
  mutate(PlotObservationID = factor(PlotObservationID)) %>%
  left_join(data_RS_CH_ID %>%
              mutate(PlotObservationID = factor(PlotObservationID)))

Save to clean data

write_tsv(final_RS_data,
          here("data", "clean","final_RS_data_SatEmb_20250919.csv"))

Session info

sessionInfo()

R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default
  LAPACK version 3.12.1

locale:
[1] LC_COLLATE=Spanish_Spain.utf8  LC_CTYPE=Spanish_Spain.utf8   
[3] LC_MONETARY=Spanish_Spain.utf8 LC_NUMERIC=C                  
[5] LC_TIME=Spanish_Spain.utf8    

time zone: Europe/Madrid
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] pracma_2.4.4     progressr_0.15.1 furrr_0.3.1      future_1.67.0    mgcv_1.9-3      
 [6] nlme_3.1-168     knitr_1.50       sf_1.0-21        dtplyr_1.3.2     here_1.0.2      
[11] lubridate_1.9.4  forcats_1.0.0    stringr_1.5.2    dplyr_1.1.4      purrr_1.1.0     
[16] readr_2.1.5      tidyr_1.3.1      tibble_3.3.0     ggplot2_4.0.0    tidyverse_2.0.0 
[21] signal_1.8-1    

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.53          bslib_0.9.0        lattice_0.22-7    
 [5] tzdb_0.5.0         vctrs_0.6.5        tools_4.5.1        generics_0.1.4    
 [9] parallel_4.5.1     proxy_0.4-27       pkgconfig_2.0.3    Matrix_1.7-4      
[13] KernSmooth_2.23-26 data.table_1.17.8  RColorBrewer_1.1-3 S7_0.2.0          
[17] lifecycle_1.0.4    compiler_4.5.1     farver_2.1.2       codetools_0.2-20  
[21] htmltools_0.5.8.1  class_7.3-23       sass_0.4.10        yaml_2.3.10       
[25] crayon_1.5.3       pillar_1.11.0      jquerylib_0.1.4    MASS_7.3-65       
[29] classInt_0.4-11    cachem_1.1.0       parallelly_1.45.1  tidyselect_1.2.1  
[33] digest_0.6.37      stringi_1.8.7      listenv_0.9.1      splines_4.5.1     
[37] rprojroot_2.1.1    fastmap_1.2.0      grid_4.5.1         cli_3.6.5         
[41] magrittr_2.0.3     utf8_1.2.6         e1071_1.7-16       withr_3.0.2       
[45] scales_1.4.0       bit64_4.6.0-1      timechange_0.3.0   rmarkdown_2.29    
[49] globals_0.18.0     bit_4.6.0          hms_1.1.3          evaluate_1.0.5    
[53] rlang_1.1.6        Rcpp_1.1.0         glue_1.8.0         DBI_1.2.3         
[57] vroom_1.6.5        rstudioapi_0.17.1  jsonlite_2.0.0     R6_2.6.1          
[61] units_0.8-7